Abstract
Abstract. In recent, the trajectory data of moving objects is getting bigger and bigger, and it has become a very important part of the social big data. Its compression is an indispensable operation of data processing, and also it is the basis of the data storage, analysis and mining of moving objects. In the related research, there are two kinds of methods for the trajectory compression. One is to compress trajectory data based on its own spatial-temporal characteristics, another kind of methods is the map-matched trajectory compression. However, for offline trajectory compression, methods based on spatial-temporal characteristics do not take the road network constraints into account. If road networks are considered, the map matching is needed first, and it will greatly affect the efficiency of trajectory compression. Therefore, this paper proposes a new trajectory compression algorithm that combines the spatial-temporal characteristics from trajectories themselves and structural characteristics from road networks to improve the compression precision and efficiency.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
